Continuous Pattern Mining Using the FCPGrowth Algorithm in Trajectory Data Warehouses

The study of tourist movements and their behavior becomes an important issue. This paper presents a tourist trajectory model based on Event Time of Interest, Region of Interest and Place of Interest. This model enables the generation of trajectory data that will be stored in a trajectory data warehouse. This article develops and constructs an ontology modeling framework that acts as the foundational model platform for the creation of a semantic trajectory data warehouse. In addition, the modeling approach describes thematic dimensions that provide a design platform for analysis and knowledge discovery in the trajectory and processing data for moving objects.

The popularization of mobile devices and satellite navigation systems has brought new opportunities and challenges to many location-based services (LBSs), intelligent transport systems (ITSs) and fleet management systems. Those systems store trajectory data in moving object databases and trajectory data warehouses. Trajectory datasets can be used in many data mining tasks, e.g., for mining traffic patterns, frequently visited places, and so on. However, such data mining tasks introduce security and location privacy threats to companies (data owners) and mobile objects that generate trajectories. In this paper, in order to overcome the location privacy threats, we present a technique that blurs trajectories according to user-defined privacy profiles and a data mining algorithm called MCSPP that is capable of mining continuous sequences from blurred trajectories. To test that our approach works correctly, we have implemented an experimental environment. Moreover, the conducted experiments have shown a good performance and scalability of the MCSPP algorithm.

With the development of pervasive systems and positioning technology, the analysis of data resulting from moving objects trajectories has attracted a particular interest. Those data are called trajectory data and are stored in a suitable repository called trajectory data warehouse (TDW). TDW view definitions are constructed from heterogeneous mobile information sources schema. Those latter are more and more autonomous and they often evolve by changing their contents and/or their schema then the TDW view definition may become undefined and consequently the analysis process may be affected. For this reason, it is important to achieve view definitions restoring or synchronization following schema changes occurred at the heterogeneous mobile information sources. The goal of this paper is to propose a TDW view definition maintenance based on mobile agent view adaptation system.

The usage of location aware devices, such as mobile phones and GPS-enabled devices, is widely spread nowadays, allowing access to large spatiotemporal datasets.The space-time nature of this kind of data results in the generation of huge amounts of trajectory data and imposes new challenges regarding their efficient management.To address this need, the traditional database technology has been extended into Moving Object Databases (MODs) that handle modeling, indexing and query processing issues for trajectories.Moreover, the analysis of such trajectory data raises opportunities for discovering behavioral patterns that can be exploited in applications like traffic management and service accessibility.Online analytical processing (OLAP) and data mining (DM) techniques have been employed in order to convert this vast amount of raw data into useful knowledge.Indicatively, the variable number of moving objects in different urban areas, the average speed of vehicles, the ups and downs of vehicles speed as well as useful insights, like discovering popular movements can be analyzed in a Trajectory Data Warehouse (TDW).

Thanks to the recent advances in energy-harvesting devices, nodes equipped with such devices are produced and enable Wireless Sensor Networks (WSNs) to be energy self-sustainable. Such networks are named as Battery-Free WSNs (BF-WSNs). Data aggregation is an essential operation in WSNs, and the Minimum Latency Aggregation Scheduling (MLAS) problem which seeks a collision-free aggregation scheduling with the minimum latency has been well studied in Battery-Powered WSNs (BP-WSN). In BP-WSNs, latency is mainly caused by the time overhead in collision-avoiding. However, the time-consumption for node recharging is the main cause of latency in BF-WSNs. Moreover, the collisions are time-independent while the recharge rate is time-varying. Therefore, the previous algorithms are not suitable for BF-WSNs. In addition, if aggregating data from all nodes, the latency would be determined by the node with the lowest recharge rate. Thus, we propose to aggregate a subset of nodes which can meet the given coverage quality requirement. Meanwhile, the aggregation tree and scheduling strategy should be adaptive to the current energy condition. We formulate this problem and propose a distributed algorithm which can select nodes adaptively according to their energy condition and schedule these nodes to achieve the minimum latency, simultaneously. To the best of our knowledge, it is the first distributed algorithm to solve the MLAS problem with coverage guarantee in BF-WSNs. The simulation results verify that our algorithm can reduce aggregation latency effectively, especially in bad energy condition.

Product and design analytics is emerging as a promising area for the analysis of large-scale data and usage of the extracted knowledge for the design of optimal system. The continuous preference trend mining (CPTM) algorithm and application proposed in this study address some fundamental challenges in the context of product and design analytics. The first contribution is the development of a new predictive trend mining technique that captures a hidden trend of customer purchase patterns from accumulated transactional data. Unlike traditional, static data mining algorithms, the CPTM does not assume stationarity but dynamically extracts valuable knowledge from customers over time. By generating trend embedded future data, the CPTM algorithm not only shows higher prediction accuracy in comparison with well-known static models but also provides essential properties that could not be achieved with previously proposed models: utilizing historical data selectively, avoiding an over-fitting problem, identifying performance information of a constructed model, and allowing a numeric prediction. The second contribution is the formulation of the initial design problem which can reveal an opportunity for multiple profit cycles. This mathematical formulation enables design engineers to optimize product design over multiple life cycles while reflecting customer preferences and technological obsolescence using the CPTM algorithm. For illustration, the developed framework is applied to an example of tablet PC design in leasing market and the result shows that the determination of optimal design is achieved over multiple life cycles.

In this paper we present a system of spatial data warehouse designed for aggregating and analyzing a wide range of spatial information. The data is generated by media meters working in a telemetric system. The data warehouse is based on a new model called the cascaded star model. The cascaded star is the spatial development of a standard star schema. We decided to use an indexing structure called aggregation tree. The aggregation tree materialization and integrated mechanism of available memory estimation highly improve the efficiency of the system. The theoretical aspects are confirmed by the presented tests results.KeywordsSpatial DataData WarehouseQuery ExecutionAggregation TreeMinimal Bounding RectangleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

As a branch of frequent pattern mining, the task-oriented pattern mining has received increasing attention due to its broad application scenarios. The lexicographic subset tree based algorithm and the multiobjective evolutionary algorithm are two effective approaches for finding the most frequent and complete pattern in task-oriented applications. However, both suffer from heavy computational cost since their runtime increases rapidly as the transaction dataset is scaled up. To address this issue, this paper regards the task-oriented pattern mining as a data-driven optimization problem and solves it by using a surrogate-assisted multiobjective evolutionary algorithm. Based on the framework of our previous multiobjective evolutionary algorithm for task-oriented pattern mining, the proposed algorithm estimates the objective values of most solutions using an ensemble of surrogates instead of the real objective functions, thereby highly improving the efficiency of the algorithm. Experimental results on three task-oriented applications indicate that the proposed algorithm has better efficiency than state-of-the-art algorithms.


 
 
 
 Apriori algorithm is a classical algorithm of association rule mining and widely used for generating frequent item sets. However, the original Apriori algorithm has some limitation such as it needs to scan the dataset many times to discover all frequent itemset and generate huge number of candidate itemset. To overcome these limitations, researchers have made a lot of improvements to the Apriori such as candidate generation, without candidate generation, transaction reduction, partitioning, and sampling. When it comes to mine massive data, these algorithms failed to prove efficiency because limitation of the processing capacity, storage capacity, and main memory constraints. Therefore, parallel and distributed algorithms are developed to perform large-scale computing in ARM on multiple processors. However, the problems with most of the parallel and distributed framework are overheads of managing distributed system, lack of high level parallel programming language, and node failures. Hadoop-MapReduce is an efficient, scalable, and simplified programming model for massive data processing and it also available on cloud environment. Cloud computing offers huge computing resources, and capacities to solve big data challenges. Recently many parallel algorithms have been proposed on Hadoop-MapReduce to enhance the performance of Apriori algorithm but there are some drawbacks: since multiple scan over the dataset is needed to generate candidate itemset, it consume more execution time. The aim of this study is to propose a parallel Transaction Reduction MapReduce Apriori algorithm (TRMR-Apriori) which is reduce unnecessary transaction values and transactions from the dataset in parallel manner to overcome above problems. The experiments show that TRMR-Apriori is able to achieve better execution time to discover frequent itemset those of previous sequential ARM algorithms such as Apriori, AprioriTid, Eclat, and FP-Growth and the previous parallel algorithms such as PApriori, MRApriori, and Modified Apriori with different condition on homogeneous computing environment using Hadoop-MapReduce platform in cloud. Overall, the TRMR-Apriori shows the strength to extract the frequent itemset from massive dataset in cloud.
 
 
 
 

Recommender systems are chiefly renowned for their applicability in e-commerce sites and social media. For system optimization, this work introduces a method of behaviour pattern mining to analyze the person's mental stability. With the utilization of the sequential pattern mining algorithm, efficient extraction of frequent patterns from the database is achieved. A candidate sub-sequence generation-and-test method is adopted in conventional sequential mining algorithms like the Generalized Sequential Pattern Algorithm (GSP). However, since this approach will yield a huge candidate set, it is not ideal when a large amount of data is involved from the social media analysis. Since the data is composed of numerous features, all of which may not have any relation with one another, the utilization of feature selection helps remove unrelated features from the data with minimal information loss. In this work, Frequent Pattern (FP) mining operations will employ the Systolic tree. The systolic tree-based reconfigurable architecture will offer various benefits such as high throughput as well as cost-effective performance. The database's frequently occurring item sets can be found by using the FP mining algorithms. Numerous research areas related to machine learning and data mining are fascinated by feature selection since it will enable the classifiers to be swift, more accurate, and cost-effective. Over the last ten years or so, there have been significant technological advancements in heuristic techniques. These techniques are beneficial because they improve the search procedure's efficiency, albeit at the potential sacrifice of completeness claims. A new recommender system for mental illness detection was based on features selected using River Formation Dynamics (RFD), Particle Swarm Optimization (PSO), and hybrid RFD-PSO algorithm is proposed in this paper. The experiments use the depressive patient datasets for evaluation, and the results demonstrate the improved performance of the proposed technique.

The upgrade of frequent item set mining to a setup with multiple relations – frequent query mining – poses many efficiency problems. Taking Object Identity as starting point, we present several optimization techniques for frequent query mining algorithms. The resulting algorithm has a better performance than a previous ILP algorithm and competes with more specialized graph mining algorithms in performance.

Frequent itemset mining is a fundamental problem in data mining area because frequent itemsets have been extensively used in reasoning, classifying, clustering, and so on. To mine frequent itemsets, previous algorithms based on a prefix tree structure have to construct many prefix trees, which is very time-consuming. In this paper, we propose a novel frequent itemset mining algorithm called DPT (Dynamic Prefix Tree) which uses only one prefix tree. We first introduce the concept of the post-conditional database of an itemset, and analyze the distribution of an itemset’s post-conditional database in a prefix tree representing a database. Subsequently, we illuminate how DPT adjusts the prefix tree to mine frequent itemsets and give three optimization techniques. An interesting advantage of DPT is that the algorithm can directly output a prefix tree representing all frequent itemsets after slight modifications. Using only one dynamic prefix tree, DPT avoids the high cost of constructing many prefix trees and thus gains significant performance improvement. Experimental results show that DPT remarkably outperforms previous algorithms with respect to running time and memory usage, and that a prefix tree representing all frequent itemsets DPT outputs can be used more efficient than a list representing them previous algorithms output.

Data mining acquires knowledge and rules that is connotative, unknown and having potential value for decision-making from large databases or data warehouses. Existing association rule mining algorithms and modules cater to a centralized environment, such as database or data warehouse. With the development of distributed database and network technology, they don't meet the needs of mining rules from distributed data sets. In this paper, we provide a mining model based on the distributed database and a corresponding effective mining algorithm. Using association rules of market basket analysis, integrate every database file, then get the mining result, and make a further mining upon the mining method, transport the rules which are not fit with the requirements back to each distributed station to make a more accurate mining process, thus avoiding the frequent network communication. This algorithm can reduce frequent communication burden, they has an distinguish virtue in parallel arithmetic computing and asynchronous operation & heterogeneous mining.

Incremental sequential patterns for multivariate temporal association rules mining

Sequential pattern mining is an important problem in continuous, fast, dynamic and unlimited stream mining. Recently approximate mining algorithms are proposed which spend too many system resources and can only obtain the partial feature of stream. In this paper, a multi-level evolving sequential pattern mining model ESPMM is presented to address this problem thus the mostly entire stream feature is obtained. Furthermore, because of the smaller support of sequential patterns in each level, a mining method BMLA based on Levenshtein-Automata is proposed which builds state conversion model to compute sequences' similarity in linear time. The experiment results show this model is effective and efficient.